<?xml version="1.0"?>
<doc>
    <assembly>
        <name>Neural.NET</name>
    </assembly>
    <members>
        <member name="T:Neural.NetworkBuilder">
            <summary>
            Summary description for NetworkBuilder.
            </summary>
        </member>
        <member name="M:Neural.NetworkBuilder.AddHiddenLayer(System.Int32)">
            Adds a hidden layer to build with the given size.
            @param size The size of the hidden layer.
        </member>
        <member name="M:Neural.NetworkBuilder.AddInputNeuron(System.String)">
            Adds an input neuron to the builder.
            @param name The name of the input neuron.
        </member>
        <member name="M:Neural.NetworkBuilder.AddInputNeurons(System.Collections.ArrayList)">
            Adds a list of input neurons to the network builder.
            @param neurons The list of neurons.
        </member>
        <member name="M:Neural.NetworkBuilder.AddOutputNeuron(System.String)">
            Adds an output neuron to the builder.
            @param name The name of the output neuron.
        </member>
        <member name="M:Neural.NetworkBuilder.AddOutputNeurons(System.Collections.ArrayList)">
             Adds a list of output neurons to the builder.
            
             @param neurons The list of neurons.
        </member>
        <member name="M:Neural.NetworkBuilder.BuildNetwork">
            Build a network.  This method causes the network builder to build a fully
            connected, feed-forward neural network.
            @return A constructed network.
        </member>
        <member name="P:Neural.NetworkBuilder.Sizes">
            Return the sizes of the layers of the network to build
            @return The sizes of the network's layers.
        </member>
        <member name="P:Neural.NetworkBuilder.ActivationFactory">
            Sets the factory that will create activation functions, therefore
            building the network with the desired activation function.
            @param factory The activation factory.
        </member>
        <member name="T:Neural.Data.RangeScaler">
            <summary>
            Scales a value that falls within a certain range, given by the min 
            and max value to a 0..1 range.
            </summary>
        </member>
        <member name="T:Neural.Data.Scaler">
            <summary>
            The scaler is an abstract class and a parent to the other scalers.
            </summary>
        </member>
        <member name="M:Neural.Data.Scaler.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="F:Neural.Data.Scaler.composite">
            <summary>
            The next type of scaling to engage in.
            </summary>
        </member>
        <member name="M:Neural.Data.Scaler.Scale(System.Double)">
            <summary>
            The abstract scaling method to implement.
            </summary>
            <param name="input">The value to scale</param>
            <returns>The scaled value</returns>
        </member>
        <member name="M:Neural.Data.Scaler.AddScaler(Neural.Data.Scaler)">
            <summary>
            Chaines another scaler to this scaler.
            </summary>
            <param name="scaler">Another scaler</param>
        </member>
        <member name="M:Neural.Data.RangeScaler.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.RangeScaler.Scale(System.Double)">
            <summary>
            Scale the value to a 0..1 range.
            </summary>
            <param name="input">The unscaled value</param>
            <returns>The scaled value</returns>
        </member>
        <member name="P:Neural.Data.RangeScaler.Min">
            <summary>
            The minimum value of the range to scale.
            </summary>
        </member>
        <member name="P:Neural.Data.RangeScaler.Max">
            <summary>
            The maximum value of the range to scale.
            </summary>
        </member>
        <member name="T:Neural.Activation.SigmoidActivationFactory">
            <summary>
            Summary description for SigmoidActivationFactory.
            </summary>
        </member>
        <member name="T:Neural.ActivationFactory">
            <summary>
            The activation factory interface.  Used by activation factories when building
            networks.
            </summary>
        </member>
        <member name="M:Neural.ActivationFactory.MakeFunction">
            <summary>
            Makes a  new activation function.
            </summary>
            <returns>The constructed activation function.</returns>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFactory.#ctor">
            <summary>
            Create a new sigmoid activation factory.
            </summary>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFactory.MakeFunction">
            <summary>
            Creates a new sigmoid activation function.
            </summary>
            <returns>A new activation function</returns>
        </member>
        <member name="T:Neural.Managers.DataStorageTag">
            <summary>
            The data storage tag contains book keeping information about the object
            stored, such as creation time.
            </summary>
        </member>
        <member name="M:Neural.Managers.DataStorageTag.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="P:Neural.Managers.DataStorageTag.Description">
            <summary>
            The description for this data storage tag.
            </summary>
        </member>
        <member name="P:Neural.Managers.DataStorageTag.CreateDate">
            <summary>
            Returns the date this vault item was created.
            </summary>
        </member>
        <member name="P:Neural.Managers.DataStorageTag.Item(System.String)">
            <summary>
            Adds a feature to the meta-data.  Note this is any kind of serializable object.
            </summary>
        </member>
        <member name="T:Neural.Network">
            <summary>
            Summary description for Network.
            </summary>
        </member>
        <member name="M:Neural.Network.#ctor">
            <summary>
            Create a new instance of a network.  By default a bias node
            is added to the network and automatically connected to any
            neurons added to the network (except input neurons).
            </summary>
        </member>
        <member name="M:Neural.Network.AddOutputNeuron(Neural.Neuron)">
            <summary>Add output neuron.</summary>
            <param name="neuron">The output neuron</param>
        </member>
        <member name="M:Neural.Network.AddInputNeuron(Neural.Neuron)">
            <summary>
            Add input neuron.  Input neurons have their value "set" to a given value
            and then propagate that value to any neurons they are connected to.
            </summary>
            <param name="neuron">The new input neuron</param>
        </member>
        <member name="M:Neural.Network.AddInternalNeuron(Neural.Neuron)">
            <summary>
            Adds a "hidden layer" neuron.  Hidden layers get their inputs from either
            input neurons or other hidden layers.  They then pass those values to the
            next output or hidden layer.
            </summary>
            <param name="neuron">The new neuron to add</param>
        </member>
        <member name="M:Neural.Network.ConnectToBias(Neural.Neuron)">
            <summary>
            Connect a neuron to the bias neuron.
            </summary>
            <param name="neuron">The bias neuron</param>
        </member>
        <member name="M:Neural.Network.Connect(System.String,System.String)">
            <summary>
            Connect two nuerons by their name.  This method is used to
            build up connections within the network.  The only connection established
            by default is the connection to the bias node.  This is the
            preferred method.
            </summary>
            <param name="baseNeuron">The name of the base neuron to connect</param>
            <param name="dendrite">The name of the dendrite neuron to connect</param>
        </member>
        <member name="M:Neural.Network.Connect(Neural.Neuron,Neural.Neuron)">
            <summary>
            Connect two neurons.  This method is used to
            build up connections within the network.  The only connection established
            by default is the connection to the bias node.
            </summary>
            <param name="baseNeuron">A base neuron to connect</param>
            <param name="dendrite">A dendrite neuron to connect</param>
        </member>
        <member name="M:Neural.Network.AddNeuron(Neural.Neuron)">
            <summary>
            Add a neuron to the network.  If a neuron with the same name has
            already been added to the network, a <code>DuplicateNeuronException</code>
            is thrown.  This need not be the same neuron, but all neurons that
            are part of this network must have unique names, with respect to
            the network.
            </summary>
            <param name="neuron">The neuron to add</param>
        </member>
        <member name="M:Neural.Network.Process(System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Process a set of inputs and return a result.  The method checks to
            see that the names in the input map correspond to the names of the input
            neurons.  If they do not match, an <code>InvalidInputsException</code>
            is thrown.  It returns a map with the output values keyed on the names
            of the output neurons.
            </summary>
            <param name="inputs">The inputs to the network</param>
            <returns>The processed result</returns>
        </member>
        <member name="M:Neural.Network.getTrainer(System.String)">
            <summary>
            Returns the trainer attached to this network.  A trainer configures
            the network for training on a given method or algorithm.
            </summary>
            <param name="neuronId">The neuron trainer for the named neuron</param>
            <returns>The neuron trainer used by a given neuron</returns>
        </member>
        <member name="M:Neural.Network.SetFeedback(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            During training a network is presented the actual and expected values
            for a given input.  Based on this, the network back-propagates the errors
            and by the derivative chaining rule, calculates the weight adjustments
            necessary to train the network, given the trainer.
            </summary>
            <param name="actual">The actual values obtained from executing the network</param>
            <param name="expected">The expected values from a training example</param>
        </member>
        <member name="M:Neural.Network.BeginTraining(Neural.Trainer)">
            <summary>
            Attaches a trainer to the network and establishes any necessary structures
            for training.
            </summary>
            <param name="trainer">The trainer that will perform the training</param>
        </member>
        <member name="M:Neural.Network.EndTraining">
            <summary>
            Completes the training by removing training structures from the
            network.
            </summary>
        </member>
        <member name="M:Neural.Network.AdjustWeights">
            <summary>
            Called during training to periodically adjust the weights
            on the network.
            </summary>
        </member>
        <member name="M:Neural.Network.UpdateAdjustments">
            <summary>
            Updates the weight adjustments for all the neurons in the network.  Note that
            this is not the same as updating the weights.  This just calculates the
            change in the weight.
            </summary>
        </member>
        <member name="P:Neural.Network.Item(System.String)">
            <summary>
            Indexer to acces individual neurons.
            </summary>
        </member>
        <member name="P:Neural.Network.NueronNames">
            <summary>
            Returns the names of all the neurons.
            </summary>
        </member>
        <member name="P:Neural.Network.ValidateInputs">
            <summary>
            Shut off validating the input keys during production runs.
            Can save some time.
            </summary>
        </member>
        <member name="P:Neural.Network.ActivationFactory">
            <summary>
            Set the activation factory which will create new activation functions.
            </summary>
            <value>factory The activation factory.</value>
        </member>
        <member name="T:Neural.Pocket">
            <summary>
            Summary description for Pocket.
            </summary>
        </member>
        <member name="M:Neural.Pocket.#ctor(System.String)">
            Creates a new instance of Pocket 
        </member>
        <member name="M:Neural.Pocket.AddError(System.String)">
            <summary>
            Adds a new error to monitor in the pocket.
            </summary>
            <param name="error">The name of the error</param>
        </member>
        <member name="M:Neural.Pocket.CloseError(System.String)">
            
             Closes named error, cleaning up the pocket.
            
             @param error The name of the error.
            
        </member>
        <member name="M:Neural.Pocket.GetContents(System.String)">
            
             Returns the contents of the pocket or null if there is no network in
             the pocket.
             @param error The name of the error.
             @return The network.
            
        </member>
        <member name="M:Neural.Pocket.GetLastUpdateEpoch(System.String)">
            
             Return the epoch the pocket was updated.  A -1 is returned if there is
             no network in the pocket.
             @param error The name of the error.
             @return The epoch number.
            
        </member>
        <member name="M:Neural.Pocket.GetLastUpdateError(System.String)">
            
             Returns the last update error for the network in the pocket or the 
             MAX_VALUE of double if there is no network in the pocket.
             @param error The name of the error.
             @return The error.
            
        </member>
        <member name="M:Neural.Pocket.GetErrors">
            
             Returns the names of the errors that are managed the this pocket.
             @return The list of error names.
            
        </member>
        <member name="M:Neural.Pocket.SaveNetwork(System.String,Neural.Network,Neural.Trainer)">
            
             Save the network to the pocket with the named error calculator.
             @param error The name of the error.
             @param network The network to save.
             @param trainer The trainer training the network.
            
        </member>
        <member name="T:Neural.ExampleSetException">
            <summary>
            Raised by the example set, if there is a problem.
            </summary>
        </member>
        <member name="M:Neural.ExampleSetException.#ctor(System.String)">
            <summary>
            Creates a new example set exception.
            </summary>
            <param name="message">The message</param>
        </member>
        <member name="T:Neural.Data.NMinusOneClassTranslator">
            <summary>
            Translates values into N-1 classes.  For example, three distinct values
            are translated into two output values (where { 0.0, 0.0 } is the third 
            class.
            </summary>
        </member>
        <member name="T:Neural.Data.Translator">
            <summary>
            Translator is the base class for translators, which take strings
            and translate them.
            </summary>
        </member>
        <member name="M:Neural.Data.Translator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="F:Neural.Data.Translator.keys">
            <summary>
            The allowable classes to translate.
            </summary>
        </member>
        <member name="F:Neural.Data.Translator.columns">
            <summary>
            The column names.  Not used?
            </summary>
        </member>
        <member name="M:Neural.Data.Translator.Translate(System.String)">
            <summary>
            The abstract translation function.  Subclasses will take a string value
            and translate it into some combination of double values, returned as an
            array.
            </summary>
            <param name="values">The raw string</param>
            <returns>The values</returns>
        </member>
        <member name="M:Neural.Data.Translator.AddKey(System.String)">
            <summary>
            Adds a new key to translate - note that all necessary keys must be
            defined in the translator.  Any values that are not defined, will
            generate a TranslatorException.  The order in which the keys are
            added is important, but dependant on the type of translation
            accomplished.
            </summary>
            <param name="keyName">The name of the class to translate</param>
        </member>
        <member name="P:Neural.Data.Translator.OutputSize">
            <summary>
            Abstract method that returns the size of an encoded class.
            </summary>
            <returns>The size of the return tags</returns>
        </member>
        <member name="M:Neural.Data.NMinusOneClassTranslator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.NMinusOneClassTranslator.Translate(System.String)">
            <summary>
            The N-1 translator returns N-1 doubles for N possible strings.  For
            example, if the strings are { "car", "plane", "train", "boat" } then
            there are 4 possible strings of 3 values { 0.0, 0.0, 0.0 } for "car",
            { 1.0, 0.0, 0.0 } for "plane", { 0.0, 1.0, 0.0 } for "traine", and
            { 0.0, 0.0, 1.0 } for "boat".  If an unexpected string is entered,
            "rocket", for example, then an exception is thrown.
            </summary>
            <param name="values">The string to be translated</param>
            <returns>The array representing this string</returns>
        </member>
        <member name="P:Neural.Data.NMinusOneClassTranslator.OutputSize">
            <summary>
            Returns the output size of the translator.
            </summary>
        </member>
        <member name="T:Neural.Calculators.SSEErrorCalculator">
            <summary>
            Sum of squares error calculator.
            </summary>
        </member>
        <member name="T:Neural.ErrorCalculator">
            <summary>
            The error calculation interface.  Error calculators will implement this interface.
            </summary>
        </member>
        <member name="M:Neural.ErrorCalculator.CalculateError(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Calculates the error based on the expected and actual values.
            </summary>
            <param name="expected">The expected values from training</param>
            <param name="actual">The actual values produced by the network</param>
            <returns>The error measure</returns>
        </member>
        <member name="P:Neural.ErrorCalculator.DefaultName">
            <summary>
            If no name is provided when registering the error calculator, this will be its name.
            </summary>
        </member>
        <member name="M:Neural.Calculators.SSEErrorCalculator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Calculators.SSEErrorCalculator.CalculateError(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Calculates the contribution of this element to the sum of squared errors (SSE).
            This is diffrent from the MSE, in that the SSE does not average the error
            over the number of elements.
            </summary>
            <param name="expected">The expected values</param>
            <param name="actual">The actual values</param>
            <returns>The contribution of the element to the SSE</returns>
        </member>
        <member name="P:Neural.Calculators.SSEErrorCalculator.DefaultName">
            <summary>
            Returns the default name for this error calculator.
            </summary>
        </member>
        <member name="T:Neural.Activation.TanhActivationFactory">
            <summary>
            Summary description for TanhActivationFactory.
            </summary>
        </member>
        <member name="M:Neural.Activation.TanhActivationFactory.#ctor">
            <summary>
            Create a new Tanh Activation factory
            </summary>
        </member>
        <member name="M:Neural.Activation.TanhActivationFactory.MakeFunction">
            <summary>
            This method creates a new Tanh activation function.
            </summary>
            <returns>A tanh activation function</returns>
        </member>
        <member name="T:Neural.Managers.InputDataManager">
            <summary>
            Maintains a repository of input data.
            </summary>
        </member>
        <member name="M:Neural.Managers.InputDataManager.#ctor">
            <summary>
            Creates a new instance of InputDataManager.
            </summary>
        </member>
        <member name="M:Neural.Managers.InputDataManager.GetManager">
            <summary>
            Returns the input data manager.
            </summary>
            <returns>The input data manager</returns>
        </member>
        <member name="M:Neural.Managers.InputDataManager.AddInputData(System.Collections.ArrayList,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Adds data to the manager.
            </summary>
            <param name="data">The data to store</param>
            <param name="name">The name of the data file</param>
            <param name="tag">The data storage tag</param>
        </member>
        <member name="M:Neural.Managers.InputDataManager.AddInputData(System.Collections.ArrayList,System.String)">
            <summary>
            Adds data to the data manager.
            </summary>
            <param name="data">The data to manage</param>
            <param name="name">The name of the data file</param>
        </member>
        <member name="M:Neural.Managers.InputDataManager.CleanVault">
            <summary>
            Cleans the vault.
            </summary>   
        </member>
        <member name="M:Neural.Managers.InputDataManager.getInputData(System.String)">
            <summary>
            Returns the data set.
            </summary>
            <param name="name">The name of the data set</param>
            <returns>The data set</returns>
        </member>
        <member name="M:Neural.Managers.InputDataManager.GetStorageTag(System.String)">
            <summary>
            Returns the data storage tag.
            </summary>
            <param name="name">The name of the data set</param>
            <returns>The data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.InputDataManager.ListDataSets">
            <summary>
            Lists the data sets in the vault.
            </summary>
            <returns>The data sets in the vault</returns>
        </member>
        <member name="M:Neural.Managers.InputDataManager.UpdateDataSet(System.Collections.ArrayList,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Updates the given data set and the storage tag.
            </summary>
            <param name="data">The data set</param>
            <param name="name">The name of the data set</param>
            <param name="tag">The data storage tag</param>
        </member>
        <member name="M:Neural.Managers.InputDataManager.UpdateDataSet(System.Collections.ArrayList,System.String)">
            <summary>
            Saving the data set without modifying the storage tag.
            </summary>
            <param name="data">The data</param>
            <param name="name">The name of the data set</param>
        </member>
        <member name="P:Neural.Managers.InputDataManager.VaultDirectory">
            <summary>
            Returns the vault directory.
            </summary>
        </member>
        <member name="T:Neural.Neuron">
            <summary>
            A neuron, along with the axon, is the fundamental building block of the
            neural network.  Each neuron maintains information about inputs, outputs,
            and feedback through the neuron.  It is coupled to another neuron through
            an axon.  Each neuron has a unique id, and no two neurons in the same
            network can have the same id.
            </summary>
        </member>
        <member name="M:Neural.Neuron.#ctor">
            <summary>
            Default constructor.  Sets the default name to a GUID.
            </summary>
        </member>
        <member name="M:Neural.Neuron.#ctor(System.String)">
            <summary>
            Constructor - requires a name which needs to be unique for the
            network.
            </summary>
            <param name="name">The name of this neuro</param>
        </member>
        <member name="M:Neural.Neuron.AddInputAxon(Neural.Axon)">
            <summary>
            Add a new input axon to the neuron.  This will adjust the weights
            and inputs appropriately.  Since this is normally used in neural
            construction - it re-randomizes the weights.
            </summary>
            <param name="axon">The new input Axon</param>
        </member>
        <member name="M:Neural.Neuron.AddOutputAxon(Neural.Axon)">
            <summary>
            Adds a new output axon to the neuron.  When the neuron fires,
            all the output axons are fired with the output value.
            </summary>
            <param name="axon">The new axon</param>
        </member>
        <member name="M:Neural.Neuron.ReceiveSignal(Neural.Axon,System.Double)">
            <summary>
            This receives an input signal from the input axon.  It then
            initializes the set of missing inputs (if necessary) with the
            ids of the input axons.  If the axon is one of the axons that
            has not sent its signal, it is removed from the set of
            missing axons and the input value is stored.  If the size of the
            missing input set drops to zero, the output value is calculate,
            and the neuron is fired.
            </summary>
            <param name="firingAxon">The axon sending signal</param>
            <param name="val">The value firing into this neuron</param>
        </member>
        <member name="M:Neural.Neuron.DotProduct(System.Double[],System.Double[])">
            <summary>
            Calculates the dot-product of two arrays.
            </summary>
            <param name="p1">First array</param>
            <param name="p2">Second array</param>
            <returns>The dot-product</returns>
        </member>
        <member name="M:Neural.Neuron.SetInput(System.Double)">
            <summary>
            If this neuron is an input neuron, then the output value is set
            by the neural network and the results are propagated to the
            output axons.
            </summary>
            <param name="val">The input value</param>
        </member>
        <member name="M:Neural.Neuron.FireNeuron">
            <summary>
            Iterates through the set of output axons, sending the output
            signal.
            </summary>
        </member>
        <member name="M:Neural.Neuron.SetError(System.Double,System.Double)">
            <summary>
            Set the error using the actual and expected values.  This 
            operates on the output nodes.  
            </summary>
            <param name="actual">The actual values obtained</param>
            <param name="expected">The expected values from an example</param>
        </member>
        <member name="M:Neural.Neuron.ReceiveFeedback(System.Double,Neural.Axon)">
            <summary>
            Receives feedback from subsequent layer neurons.  As error
            information percolates back, the neuron will calculate its delta
            and partials.
            </summary>
            <param name="source">The axon sending the feedback</param>
            <param name="val">The feedback value</param>
        </member>
        <member name="M:Neural.Neuron.SetNeuralTrainer(Neural.NeuronTraining)">
            <summary>
            Set the neuron trainer for this neuron.
            </summary>
            <param name="trainer">The neuron trainer</param>
        </member>
        <member name="M:Neural.Neuron.GetTrainer">
            <summary>
            Returns the affiliated neuron trainer.
            </summary>
            <returns>The neuron trainer attached to this neuron</returns>
        </member>
        <member name="M:Neural.Neuron.AdjustWeights">
            <summary>
            Adjust the weights.
            </summary>
        </member>
        <member name="P:Neural.Neuron.Inputs">
            <summary>
            Returns the inputs for this neuron.
            </summary>
        </member>
        <member name="P:Neural.Neuron.Name">
            <summary>
            Returns the name of this neuron.
            </summary>
        </member>
        <member name="P:Neural.Neuron.ActivationFunction">
            <summary>
            Sets the activation function used by this neuron.  The default
            activation function is the Sigmoid activation function.  The
            activation function must implement the ActivationFunction
            interface.
            </summary>
        </member>
        <member name="P:Neural.Neuron.OutputValue">
            <summary>
            Returns the output value for this neuron.  Usually called for
            output neurons by the neural network.
            </summary>
        </member>
        <member name="P:Neural.Neuron.Weights">
            <summary>
            The weight vector for the neuron.
            </summary>
        </member>
        <member name="T:Neural.Managers.NetworkManager">
            <summary>
            Manages serialized, trained networks.
            </summary>
        </member>
        <member name="M:Neural.Managers.NetworkManager.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Managers.NetworkManager.GetManager">
            <summary>
            Returns an instance of the network manager.
            </summary>
            <returns>The network manager</returns>
        </member>
        <member name="M:Neural.Managers.NetworkManager.AddNetwork(Neural.Network,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Add a new network to the vault.
            </summary>
            <param name="name">The name of the network</param>
            <param name="network">The network</param>
            <param name="tag">The storage tag for this network</param>
        </member>
        <member name="M:Neural.Managers.NetworkManager.addNetwork(Neural.Network,System.String)">
            <summary>
            Add a network to the vault, using the default data storage tag.
            </summary>
            <param name="name">The name of the network</param>
            <param name="network">The network</param>
        </member>
        <member name="M:Neural.Managers.NetworkManager.CleanVault">
            <summary>
            Cleans the vault of all networks.
            </summary>
        </member>
        <member name="M:Neural.Managers.NetworkManager.getNetwork(System.String)">
            <summary>
            Returns the named network from the vault.
            </summary>
            <param name="name">The name of the network</param>
            <returns>The network</returns>
        </member>
        <member name="M:Neural.Managers.NetworkManager.GetStorageTag(System.String)">
            <summary>
            Returns the data storage tag for the named network.
            </summary>
            <param name="name">The name of the network</param>
            <returns>The data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.NetworkManager.ListNetworks">
            <summary>
            Lists the networks in the vault.
            </summary>
            <returns>A list of the networks in the vault</returns>
        </member>
        <member name="M:Neural.Managers.NetworkManager.RemoveNetwork(System.String)">
            <summary>
            Remove the network from the vault.
            </summary>
            <param name="name">The name of the network to remove</param>
        </member>
        <member name="M:Neural.Managers.NetworkManager.UpdateNetwork(Neural.Network,System.String)">
            <summary>
            Update the given network without changing the data storage tag.
            </summary>
            <param name="name">The name of the network to update</param>
            <param name="network">network The network data</param>
        </member>
        <member name="M:Neural.Managers.NetworkManager.UpdateNetwork(Neural.Network,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Update the given network and the data storage tag.
            </summary>
            <param name="name">The name of the network</param>
            <param name="network">The network data</param>
            <param name="tag">The storage tag to update</param>
        </member>
        <member name="P:Neural.Managers.NetworkManager.VaultDirectory">
            <summary>
            Returns the base directory for the vault.
            </summary>
        </member>
        <member name="T:Neural.Example">
            <summary>
            An example is a set of input values combined with their
            expected values.
            </summary>
        </member>
        <member name="M:Neural.Example.#ctor">
            <summary>
            Default Constructor.
            </summary>
        </member>
        <member name="P:Neural.Example.Expected">
            <summary>
            The expected values.
            </summary>
        </member>
        <member name="P:Neural.Example.Inputs">
            <summary>
            The input values.
            </summary>
        </member>
        <member name="T:Neural.Data.KClassTranslator">
            <summary>
            Translates a value into one of k classes, using a vector
            of doubles.
            </summary>
        </member>
        <member name="M:Neural.Data.KClassTranslator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.KClassTranslator.Translate(System.String)">
            <summary>
            Translates a string value to a series of double values.  For each
            expected string value there is a corresponding value.  So, if there
            are K strings { k1, k2, ... kn } The resulting array of double is size
            K.  For example,  { "car", "plane", "boat", "train" } is a set of
            4 doubles where { 1.0, 0.0, 0.0, 0.0 } for "car", { 0.0, 1.0, 0.0, 0.0 }
            for plane, etc.  If an unexpected string is sent in "rocket", in our
            example, then an exception is thrown.
            </summary>
            <param name="values">The raw string value</param>
            <returns>An array of doubles which represent the string value</returns>
        </member>
        <member name="P:Neural.Data.KClassTranslator.OutputSize">
            <summary>
            Returns the output size of the translator.
            </summary>
        </member>
        <member name="T:Neural.Listeners.MaxIterationsListener">
            <summary>
            Stops training when the trainer has exceeded the maximum
            number of iterations.
            </summary>
        </member>
        <member name="M:Neural.Listeners.MaxIterationsListener.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Listeners.MaxIterationsListener.#ctor(System.Int32)">
            <summary>
            Constructor that sets the maximum number of iterations.
            </summary>
            <param name="val">The maximum number of iterations</param>
        </member>
        <member name="M:Neural.Listeners.MaxIterationsListener.EpochEnd(Neural.TrainingEpochEvent)">
            <summary>
            Implements the listener interface and handles the end of epoch event.
            If ther number of training iterations is &gt; the maximum iterations
            value, then training is stopped.
            </summary>
            <param name="evt">The epoch end event</param>
        </member>
        <member name="P:Neural.Listeners.MaxIterationsListener.MaxIterations">
            <summary>
            The maximum number of iterations.
            </summary>
        </member>
        <member name="T:Neural.DuplicateNeuronException">
            <summary>
            Summary description for DuplicateNeuronException.
            </summary>
        </member>
        <member name="M:Neural.DuplicateNeuronException.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.DuplicateNeuronException.#ctor(System.String)">
            <summary>
            Constructor that takes a message.
            </summary>
            <param name="message">Message is passed to superclass</param>
        </member>
        <member name="M:Neural.DuplicateNeuronException.#ctor(System.String,System.Exception)">
            <summary>
            Constructor with originating exception and message.
            </summary>
            <param name="message">The message.</param>
            <param name="excp">The originating exception</param>
        </member>
        <member name="T:Neural.ResultSet">
            <summary>
            Summary description for ResultSet.
            </summary>
        </member>
        <member name="M:Neural.ResultSet.#ctor">
            Creates a new instance of ResultSet 
        </member>
        <member name="M:Neural.ResultSet.AddResult(Neural.Result)">
            Add a result to the result set.
            @param result A new result
        </member>
        <member name="P:Neural.ResultSet.Item(System.Int32)">
            Returns the result at the given index.
            @param index The index of the result.
            @return The result.
        </member>
        <member name="P:Neural.ResultSet.Count">
            Returns the size of the result set.
            @return The size.
        </member>
        <member name="T:Neural.Managers.ExampleSetManager">
            <summary>
            Manages serialization of example sets.
            </summary>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.#ctor">
            <summary>
            Creates a new instance of ExampleSetManager 
            </summary> 
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.GetManager">
            <summary>
            Used to obtain a reference to the example set manager, a singleton in this
            system.
            </summary>
            <returns>The manager</returns>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.ListDataFiles">
            <summary>
            Lists the example sets in this vault.
            </summary>
            <returns>The list of example sets stored in this vault</returns>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.AddDataFile(Neural.ExampleSet,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Adds a new example set to the vault with the given name and the given
            storage tag.
            </summary>
            <param name="eset">The example set to store</param>
            <param name="name">The name of the example set</param>
            <param name="tag">The Data Storage Tag for the example set</param>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.AddDataFile(Neural.ExampleSet,System.String)">
            <summary>
            Adds a new example set to the vault.  Calls the <code>addDataFile</code>
            method with a new empty Data Storage Tag.
            </summary>
            <param name="eset">The example set to store</param>
            <param name="name">The name of the example set</param>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.UpdateDataFile(Neural.ExampleSet,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Updates the given data file and data storage tag.
            </summary>
            <param name="eset">The data to update</param>
            <param name="name">The name of the data set</param>
            <param name="tag">The data storage tag for the data</param>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.updateDataFile(Neural.ExampleSet,System.String)">
            <summary>
            Calls update data file but does not update the data storage tag.
            </summary>
            <param name="eset">The example set to update</param>
            <param name="name">The name of the example set</param>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.getStorageTag(System.String)">
            <summary>
            Returns the data storage tage associates with a given example set.
            </summary>
            <param name="name">name The name of the example set</param>
            <returns>The associated data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.GetData(System.String)">
            <summary>
            Returns the example set for the given name.
            </summary>
            <param name="name">The name of the example set</param>
            <returns>The example set</returns>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.RemoveDataFile(System.String)">
            <summary>
            Remove the data file from the vault.
            </summary>
            <param name="name">The name of the data file</param>
        </member>
        <member name="M:Neural.Managers.ExampleSetManager.CleanVault">
            <summary>
            Cleans out the example sets from the vault.
            </summary>
        </member>
        <member name="P:Neural.Managers.ExampleSetManager.VaultDirectory">
            <summary>
            Sets the directory for the example set vault.
            </summary>
        </member>
        <member name="T:Neural.Listeners.StopOnErrorListener">
            <summary>
            Stops training when the error falls to the given value.
            </summary>
        </member>
        <member name="M:Neural.Listeners.StopOnErrorListener.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Listeners.StopOnErrorListener.#ctor(System.Double)">
            <summary>
            Constructor that sets the error threshold.
            </summary>
            <param name="val">The error threshold</param>
        </member>
        <member name="M:Neural.Listeners.StopOnErrorListener.EpochEnd(Neural.TrainingEpochEvent)">
            <summary>
            An implementation of the <CODE>EpochEventListener</CODE> interface.
            Terminates training once the training error drops below the threshold
            value.
            </summary>
            <param name="evt">The epoch event</param>
        </member>
        <member name="P:Neural.Listeners.StopOnErrorListener.ErrorName">
            <summary>
            Returns the name of the error to listen to.  This allows
            multiple stop on error listeners t listend to different
            errors.
            </summary>
        </member>
        <member name="P:Neural.Listeners.StopOnErrorListener.Error">
            <summary>
            Returns the error threshold.
            </summary>
        </member>
        <member name="T:Neural.Data.RealField">
            <summary>
            A real valued field.  Essentially just a parsing of the input value.
            </summary>
        </member>
        <member name="T:Neural.Data.Field">
            <summary>
            The field for a given input reader.
            </summary>
        </member>
        <member name="M:Neural.Data.Field.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.Field.Process(System.String)">
            <summary>
            Abstract process method.  The implementation of this method translates
            a set of string, read from the data source, into an array of doubles.
            Normally, a real valued field translates a string value to an array of
            one double valued number.
            </summary>
            <param name="val">The raw string to process</param>
            <returns>The list of doubles that the given string translates into</returns>
        </member>
        <member name="P:Neural.Data.Field.Name">
            <summary>
            The field name.  Used to identify the field.
            </summary>
        </member>
        <member name="M:Neural.Data.RealField.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.RealField.Process(System.String)">
            <summary>
            Process the input string into a real value.
            </summary>
            <param name="val">The unparsed string</param>
            <returns>The double value.</returns>
        </member>
        <member name="T:Neural.Activation.SigmoidActivationFunction">
            <summary>
            Implements the sigmoid activation function.
            </summary>
        </member>
        <member name="T:Neural.ActivationFunction">
            <summary>
            The activation function inetrface.  Activation functions will implement this interface
            </summary>
        </member>
        <member name="M:Neural.ActivationFunction.Activation(System.Double)">
            <summary>
            Calculates the activation value for the network inputs.
            </summary>
            <param name="val"> The raw value</param>
            <returns>The scaled value</returns>
        </member>
        <member name="M:Neural.ActivationFunction.Derivative(System.Double)">
            <summary>
            Calculates the derivative of the network inputs.
            </summary>
            <param name="val">The raw value</param>
            <returns>The derivative of the transfer function based on that value</returns>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFunction.#ctor">
            <summary>
            Default constructor.  Sets the old value to some 
            unattainable value - to gauranteee recalculation on
            first run.
            </summary>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFunction.Recalculate(System.Double)">
            <summary>
            Recalculate the output values, if the error has changes.
            </summary>
            <param name="val">The current value</param>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFunction.Activation(System.Double)">
            <summary>
            Obtain the activation output for the given value, using
            the sigmoid scaling function.
            </summary>
            <param name="val">The value to be scaled</param>
            <returns>The scaled value</returns>
        </member>
        <member name="M:Neural.Activation.SigmoidActivationFunction.Derivative(System.Double)">
            <summary>
            Obtain the derivative of the scaled value.
            </summary>
            <param name="val">The value to be scaled</param>
            <returns>The derivative of the sigmoid at that point</returns>
        </member>
        <member name="T:Neural.Data.TranslatorException">
            <summary>
            Summary description for TranslatorException.
            </summary>
        </member>
        <member name="M:Neural.Data.TranslatorException.#ctor">
            <summary>
            Create a new translation exception.
            </summary>
        </member>
        <member name="M:Neural.Data.TranslatorException.#ctor(System.String)">
            <summary>
            Create a new translation exception
            </summary>
            <param name="message">The message</param>
        </member>
        <member name="M:Neural.Data.TranslatorException.#ctor(System.String,System.Exception)">
            <summary>
            Create a new translation excetion
            </summary>
            <param name="msg">The message</param>
            <param name="ex">The parent exception.</param>
        </member>
        <member name="T:Neural.Activation.TanhActivationFunction">
            <summary>
            The Tanh (hyperbolic tangent) activation function.
            </summary>
        </member>
        <member name="M:Neural.Activation.TanhActivationFunction.#ctor">
            <summary>
            Create a new Tanh Activation function.
            </summary>
        </member>
        <member name="F:Neural.Activation.TanhActivationFunction.oldValue">
            <summary>
            The old value - cached.
            </summary>
        </member>
        <member name="F:Neural.Activation.TanhActivationFunction.oldActivation">
            <summary>
            The activation - cached.
            </summary>
        </member>
        <member name="F:Neural.Activation.TanhActivationFunction.oldDerivative">
            <summary>
            The derivative cached.
            </summary>
        </member>
        <member name="M:Neural.Activation.TanhActivationFunction.Activation(System.Double)">
            <summary>
            Returns the tanh activation.
            </summary>
            <param name="val">The value</param>
            <returns>The activaiton value</returns>
        </member>
        <member name="M:Neural.Activation.TanhActivationFunction.Derivative(System.Double)">
            <summary>
            Returns the tanh derivative.
            </summary>
            <param name="val">The parameter value</param>
            <returns>The derivative</returns>
        </member>
        <member name="M:Neural.Activation.TanhActivationFunction.Recalculate(System.Double)">
            <summary>
            Recalculate the activation value and derivative, if necessary.
            </summary>
            <param name="val">The value</param>
        </member>
        <member name="T:Neural.Managers.PocketManager">
            <summary>
            The pocket manager manages a pocket of trained networks.
            </summary>
        </member>
        <member name="M:Neural.Managers.PocketManager.#ctor(System.String,System.String)">
            <summary>
            Creates a new instance of PocketManager
            </summary>
            <param name="error">The name of the error used for the pocket</param>
            <param name="pocketName">The name of the pocket</param>
        </member>
        <member name="M:Neural.Managers.PocketManager.SaveNetwork(Neural.Network,Neural.Trainer)">
            <summary>
            Saves the network to the pocket if it has a lower error than
            the existing version of the network.
            </summary>
            <param name="network">The network to add to the pocket</param>
            <param name="trainer">The trainer training the network</param>
        </member>
        <member name="M:Neural.Managers.PocketManager.Cleanup">
            <summary>
            Clean up the pocket contents at the end of training.
            </summary>   
        </member>
        <member name="M:Neural.Managers.PocketManager.GetNetwork">
            <summary>
            Returns the network from the pocket.
            </summary>
            <returns>The network in pocket</returns>
        </member>
        <member name="M:Neural.Managers.PocketManager.GetLastUpdateEpoch">
            <summary>
            Returns the epoch number when the pocket was last updated.
            </summary>
            <returns>The epoch number</returns>
        </member>
        <member name="M:Neural.Managers.PocketManager.GetLastUpdateError">
            <summary>
            Returns the error of the network in the pocket.
            </summary>
            <returns>The error</returns>
        </member>
        <member name="T:Neural.Managers.DataReaderManager">
            <summary>
            Stores and manages collections of saved data readers.
            </summary>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.#ctor">
            <summary>
            Creates a new instance of DataReaderManager
            </summary>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.getManager">
            <summary>
            Returns an instance of the data reader manager.
            </summary>
            <returns>The data reader manager</returns>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.AddNetwork(Neural.Data.DataReader,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Add a new data reader to the vault.
            </summary>
            <param name="name">The name of the network</param>
            <param name="reader">The data reader</param>
            <param name="tag">The storage tag for this network</param>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.AddNetwork(Neural.Data.DataReader,System.String)">
            <summary>
            Add a data reader to the vault, using the default data storage tag.
            </summary>
            <param name="name">The name of the network</param>
            <param name="reader">The data reader</param>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.CleanVault">
            <summary>
            Cleans the vault of all data readers.
            </summary>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.GetReader(System.String)">
            <summary>
            Returns the named data reader from the vault.
            </summary>
            <param name="name">The name of the data reader</param>
            <returns>The data reader</returns>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.GetStorageTag(System.String)">
            <summary>
            Returns the data storage tag for the named data reader.
            </summary>
            <param name="name">The name of the data reader</param>
            <returns>The data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.ListReaders">
            <summary>
            Lists the data readers in the vault.
            </summary>
            <returns>A list of the data readers in the vault</returns>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.RemoveReader(System.String)">
            <summary>
            Remove the data reader from the vault.
            </summary>
            <param name="name">The name of the data reader to remove</param>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.UpdateReader(Neural.Data.DataReader,System.String)">
            <summary>
            Update the given data reader without changing the data storage tag.
            </summary>
            <param name="name">The name of the data reader to update</param>
            <param name="reader">The data reader configuration</param>
        </member>
        <member name="M:Neural.Managers.DataReaderManager.UpdateReader(Neural.Data.DataReader,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Update the given data reader and the data storage tag.
            </summary>
            <param name="name">The name of the network</param>
            <param name="reader">The data reader configuration</param>
            <param name="tag">The storage tag to update</param>
        </member>
        <member name="P:Neural.Managers.DataReaderManager.VaultDirectory">
            <summary>
            Returns the base directory for the vault.
            </summary>
        </member>
        <member name="T:Neural.Data.FileDataReader">
            <summary>
            Reads a data set from a file.
            </summary>
        </member>
        <member name="T:Neural.Data.DataReader">
             <summary>
            
             <p>
             This is an abstract data reader.  A data reader will pull data from
             some source, like a file or JDBC connection, and return a data set.
             The input fields are optionally scaled or translated.  For example,
             a data file with the fields <br/>
             <code>30, 0.005, green</code><br/>
             <code>12, 0.006, blue</code><br/>
             <code>87, 0.002, red</code><br/>
             Could be translated to:<br/>
             <code>0.30, 0.5, 0.1, 0.9, 0.1</code><br/>
             <code>0.12, 0.6, 0.1, 0.1, 0.9</code><br/>
             <code>0.87, 0.2, 0.9, 0.1, 0.1</code><br/>
             <br/>
             The translators handle the changing of text input to numeric input and
             scalers adjust the range of the numeric input.  The raw input would first
             be translated to:<br/>
             <code>30.0, 0.005, 0.0, 1.0, 0.0</code><br/>
             <code>12.0, 0.006, 0.0, 0.0, 1.0</code><br/>
             <code>87.0, 0.002, 1.0, 0.0, 0.0</code><br/>
             <br/>
             Then it would be scaled to:<br/>
             <code>0.30, 0.5, 0.1, 0.9, 0.1</code><br/>
             <code>0.12, 0.6, 0.1, 0.1, 0.9</code><br/>
             <code>0.87, 0.2, 0.9, 0.1, 0.1</code><br/>
             </p>
             <p>
             Returning a raw list of values, however, would require the user to find
             a value remembering the position of the value.  For example wieght is the 
             3rd value, and height is the 17th.  Instead, the result of reading data
             is returned as a list of maps.  The list preserves the order in which data 
             was read.  The rows in the list, however, are maps based on name-value 
             pairs.  The names are taken from the column definitions.
             </p>
             <p>
             The basic operation of the data reader is first to define input columns. 
             Any translations are defined at this point.  The columns are defined, and
             any scaling to be applied to raw input values.  The readValues method, 
             which is implemented in a concrete subclass, returns name value pairs where
             the name is taken from the column definition.
             </p>
            
             </summary>
        </member>
        <member name="M:Neural.Data.DataReader.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="F:Neural.Data.DataReader.fields">
            <summary>
            The fields for this data reader.
            </summary>
        </member>
        <member name="F:Neural.Data.DataReader.columns">
            <summary>
            The columns for this data reader.
            </summary>
        </member>
        <member name="F:Neural.Data.DataReader.fieldByName">
            <summary>
            The fields indexed by name.
            </summary>
        </member>
        <member name="F:Neural.Data.DataReader.columnByName">
            <summary>
            The columsn indexed by name.
            </summary>
        </member>
        <member name="M:Neural.Data.DataReader.DefineColumn(System.String,Neural.Data.Scaler)">
            <summary>
            Define a new column in the output set with the given name and scaler.
            </summary>
            <param name="name">The column name</param>
            <param name="scaler">The scaler used to transform the column</param>
        </member>
        <member name="M:Neural.Data.DataReader.DefineColumn(System.String)">
            <summary>
            Define a column in the output set, that does not scale.
            </summary>
            <param name="name">The column name</param>
        </member>
        <member name="M:Neural.Data.DataReader.DefineRealInputField(System.String)">
            <summary>
            Defines a real valued input field in the data set with the given
            name.
            </summary>
            <param name="name">The field name</param>
        </member>
        <member name="M:Neural.Data.DataReader.DefineTranslatedInputField(System.String,Neural.Data.Translator)">
            <summary>
            Defines a translated input field with the given name.  Normally translated
            input fields are used for classifiers.  An example is translating a color
            to a set of K classes uses the KClassTranslator.
            </summary>
            <param name="name">The name of the input field</param>
            <param name="translator">The translator used to modify the value</param>
        </member>
        <member name="M:Neural.Data.DataReader.TranslateLine(System.String[])">
            <summary>
            Performs translation on a raw line.  When a line is read in from a data
            source it is parsed into an array of strings.  The array of strings
            is translated into an array of doubles.
            </summary>
            <param name="aLine">The array of strings</param>
            <returns>The doubles after translating the strings</returns>
        </member>
        <member name="M:Neural.Data.DataReader.ScaleLine(System.Double[])">
            <summary>
            Performs the scaling on a line translated into doubles.  After a line
            is read and translated into an array of doubles, it is scaled accordingly.
            The scaling is defined using the <c>defineColumn</c> method.
            </summary>
            <param name="rawLine">The raw double values read in</param>
            <returns>The scaled double values</returns>
        </member>
        <member name="M:Neural.Data.DataReader.ReadValues">
            <summary>
            This is an abstract method that is implemented by a particular data reader,
            for example a JDBC data reader would read from a database.  The result is
            a list of name-value pairs, where the names are assigned by the column
            names.
            </summary>
            <returns>A list of name-value pairs</returns>
        </member>
        <member name="M:Neural.Data.DataReader.NameValues(System.Double[])">
            <summary>
            Assigns the name to values.  The data reader's output consists of name-value
            pairs.  This matches a value to a column name.
            </summary>
            <param name="scaledLine">The inputs after scaling</param>
            <returns>The collection of doubles with their names attached</returns>
        </member>
        <member name="M:Neural.Data.FileDataReader.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.FileDataReader.OpenFile">
            <summary>
            Attempts to open the file, throwing a file not found exception,
            if necessary.
            </summary>
        </member>
        <member name="M:Neural.Data.FileDataReader.ReadNextLine">
            <summary>
            Reads the next line from the file.  Each time it is called, it reads
            a new line from the file.  If there are blank lines at the end of the
            file, a null value is returned.
            </summary>
            <returns>The array of read strings</returns>
        </member>
        <member name="M:Neural.Data.FileDataReader.ReadValues">
            <summary>
            An implementation of <code>DataReader.readValues()</code>.  Reads in the
            delimited values and returns the data.  If there is a problem, throws
            a DataReadingException.
            </summary>
            <returns>The list of data rows as name-value pairs</returns>
        </member>
        <member name="P:Neural.Data.FileDataReader.Delimiter">
            <summary>
            The delimiter that separates values.
            </summary>
        </member>
        <member name="P:Neural.Data.FileDataReader.Filename">
            <summary>
            The filename to read.
            </summary>
        </member>
        <member name="T:Neural.Managers.ResultSetManager">
            <summary>
            Mnaages the serialization of result sets.
            </summary>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.#ctor">
            <summary>
            Creates a new instance of ResultSetManager
            </summary>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.GetManager">
            <summary>
            Returns an instance of the result set manager.
            </summary>
            <returns>The result set manager</returns>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.AddInputData(Neural.ResultSet,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Adds a result set to the vault with the given storage tag.
            </summary>
            <param name="data">The result set</param>
            <param name="name">The name of throws the result set</param>
            <param name="tag">The data storage tag</param>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.addInputData(Neural.ResultSet,System.String)">
            <summary>
            Add result sets to the vault.
            </summary>
            <param name="data">The result set</param>
            <param name="name">The name of the result set</param>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.CleanVault">
            <summary>
            Cleans the results sets from the vault.
            </summary>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.GetInputData(System.String)">
            <summary>
            Returns the named result set.
            </summary>
            <param name="name">The name of the result set</param>
            <returns>The result set</returns>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.GetStorageTag(System.String)">
            <summary>
            Returns the data storage tag for the result set.
            </summary>
            <param name="name">The name of the result set</param>
            <returns>the Data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.ListResultSets">
            <summary>
            Lists the result set in the vaults.
            </summary>
            <returns>The list of data sets in the vault</returns>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.UpdateDataSet(Neural.ResultSet,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Update the result set in the vault with the given storage tag.
            </summary>
            <param name="data">The result set</param>
            <param name="name">The name of the storage tag</param>
            <param name="tag">The data storage tag</param>
        </member>
        <member name="M:Neural.Managers.ResultSetManager.UpdateDataSet(Neural.ResultSet,System.String)">
            <summary>
            Update the data set in the vault with the data and name.
            </summary>
            <param name="data">The result set</param>
            <param name="name">The name of the result set</param>
        </member>
        <member name="P:Neural.Managers.ResultSetManager.VaultDirectory">
            <summary>
            Returns the vault directory for the result sets.
            </summary>
        </member>
        <member name="T:Neural.Calculators.MSEErrorCalculator">
            <summary>
            Mean squared error calculator.
            </summary>
        </member>
        <member name="M:Neural.Calculators.MSEErrorCalculator.#ctor(System.Double)">
            <summary>
            Default constructor.  Takes as an argument the number of elements
            used to calculate the mean.
            </summary>
            <param name="sz">The number of elements to average over</param>
        </member>
        <member name="M:Neural.Calculators.MSEErrorCalculator.CalculateError(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Calculated the mean squared error contribution from these expected and
            actual values.  Note that we calculate the values, divided by the total
            number of elements, even though we are only looking at one element.  In
            the error manager, these are summed.  So, we return this element's contribution
            to the overall error.
            </summary>
            <param name="expected">The expected values</param>
            <param name="actual">The actual values</param>
            <returns>The contribution to Mean Squared Error</returns>
        </member>
        <member name="P:Neural.Calculators.MSEErrorCalculator.DefaultName">
            <summary>
            The default name for this error calcualtor.
            </summary>
        </member>
        <member name="T:Neural.EpochEventFunction">
            <summary>
            A delegate method to be called when important things happen
            during the training epoch.
            </summary>
        </member>
        <member name="T:Neural.Trainer">
            <summary>
            Summary description for Trainer.
            </summary>
        </member>
        <member name="F:Neural.Trainer.EndEpochDelegates">
            <summary>
            The delegates to be called at the end of training.
            </summary>
        </member>
        <member name="F:Neural.Trainer.StartTrainingDelegates">
            <summary>
            Called when training is started.
            </summary>
        </member>
        <member name="F:Neural.Trainer.EndPresentationDelegates">
            <summary>
            Called when each presentation is finished
            </summary>
        </member>
        <member name="F:Neural.Trainer.PostUpdateDelegates">
            <summary>
            Used to notify after an update.
            </summary>
        </member>
        <member name="M:Neural.Trainer.AddCalculator(Neural.ErrorCalculator)">
            <summary>
            The error calculator computes the error from the input values
            and the output values.  By default this is sum of squares.  This
            version of the addCalculator uses the default name for the 
            calculator.
            </summary>
            <param name="calculator">The calculator to set</param>
        </member>
        <member name="M:Neural.Trainer.AddCalculator(System.String,Neural.ErrorCalculator)">
            <summary>
            The error calculator computes the error from the input values
            and the output values.  By default this is the sum of squares.
            </summary>
            <param name="name">The name of the error calculator</param>
            <param name="calculator">The calculator to set</param>
        </member>
        <member name="M:Neural.Trainer.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Trainer.Train(Neural.Network)">
            <summary>
             <p>
            Called to train the network.  Iterates until the terminating flag is
            set.  If there are no training event listeners set then there is a
            hard termination limit of 10000 (by default) iterations.
            </p>
            <p>
            The training algorithm is simple.  It presents the training examples
            in order, up to epochSize before ending the epoch.  At that point the
            error for the epoch is calculated and the EndEpochEvent is sent to all
            the listeners.  The weights of the network are then adjusted.  Training
            stops when the hard limit is reached (given no listeners) or by a listener
            requesting a termination.
            </p>
            </summary>
            <param name="network"></param>
        </member>
        <member name="M:Neural.Trainer.UpdateWeightAdjustments">
            <summary>
            Causes the weight adjustments to be updated at all the neuron trainers.
             </summary>
        </member>
        <member name="M:Neural.Trainer.UpdateNetwork(Neural.Network)">
            <summary>
            Called at the end of an epoch.  In this case it sends the end epoch
            message to all the neuron trainers.
            </summary>
            <param name="network">The network being trained</param>
        </member>
        <member name="M:Neural.Trainer.GetTraining">
            <summary>
            Returns the default neuron training for this trainer.  It
            also sets the learning rate as appropriate.
            </summary>
            <returns>The neuron trainer used by this trainer.</returns>
        </member>
        <member name="M:Neural.Trainer.RequestTermination">
            <summary>
            Used primarily by the epoch event listeners to request the
            neural network to stop training.
            </summary>
        </member>
        <member name="P:Neural.Trainer.HardMax">
            <summary>
            If no EpochEventListeners are added to the network the hardMax
            limit is used to safely stop training.
            </summary>
        </member>
        <member name="P:Neural.Trainer.LearningRate">
            <summary>
            Set the default learning rate used for training.  The default
            is 0.20.  This is the learning rate passed to all the neuron
            trainers when they are created.
            </summary>
        </member>
        <member name="P:Neural.Trainer.Momentum">
            <summary>
            Returns the momentum used during training.  This is the
            default momentum passed to the neuron trainers when they are
            created.  The default is 0.00.
            </summary>
        </member>
        <member name="P:Neural.Trainer.EpochSize">
            <summary>
            The epoch size is the number of examples to present during training
            before the error is saved and the weights are adjusted.  Normally,
            this is equal to the size of the training set.
            </summary>
        </member>
        <member name="P:Neural.Trainer.TrainingExamples">
            <summary>
            The training examples are set of known inputs and their outputs
            used to train the network.  Normally the data is divided into
            a training set and a validation set.  The training set is used
            to train the network while the validation set is used to test
            the quality of the training.
            </summary>
        </member>
        <member name="P:Neural.Trainer.EpochCount">
            <summary>
            Returns the epoch count so far
            </summary>
        </member>
        <member name="P:Neural.Trainer.ErrorManager">
            <summary>
            Returns the error manager used by the trainer when
            calculating training error.
            </summary>
        </member>
        <member name="P:Neural.Trainer.DefaultErrorName">
            <summary>
            Sets the default error name to examine.  If no error is
            specified, the default error name is used.
            </summary>
        </member>
        <member name="T:Neural.Result">
            <summary>
            Summary description for Result.
            </summary>
        </member>
        <member name="M:Neural.Result.#ctor(System.Collections.Hashtable,System.Collections.Hashtable)">
            Creates a new instance of Result
            @param inputs The inputs for the result.
            @param outputs The expected outputs.
        </member>
        <member name="P:Neural.Result.Inputs">
            Returns the inputs for this result.
            @return The inputs.
        </member>
        <member name="P:Neural.Result.Outputs">
            The outputs for this result
            @return The expected outputs.
        </member>
        <member name="T:Neural.ExampleSet">
            <summary>
            An example set is a collection of examples, which can be subdivided,
            shuffled and manipulated.
            </summary>
        </member>
        <member name="M:Neural.ExampleSet.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.ExampleSet.AddExample(Neural.Example)">
            <summary>
            Add a new example to the set.
            </summary>
            <param name="example">A new example</param>
        </member>
        <member name="M:Neural.ExampleSet.NextExample">
            <summary>
            Move to the next example in the example set.  Returns null
            if the set is empty, and will wrap around when it gets to the
            end of the set.
            </summary>
            <returns>The next example.</returns>
        </member>
        <member name="M:Neural.ExampleSet.MakeExamples(System.Collections.Generic.List{System.Collections.Generic.Dictionary{System.String,System.Double}},System.Collections.Generic.List{System.String},System.Collections.Generic.List{System.String})">
            <summary>
            Constructs and example set from data read in as key, value pairs.  takes an
            array of input keys and ouptut keys to divide the raw data into a set of 
            inputs and expected values.
            </summary>
            <param name="data">A list of name value pairs of raw data</param>
            <param name="inputs">A list of names of input values</param>
            <param name="outputs">A list of names of output values</param>
            <returns>A constructed example set</returns>
        </member>
        <member name="M:Neural.ExampleSet.CheckSizeLimits(System.Int32[])">
            <summary>
            Checks to see the sizes are less than or equal to the example size.
            (Used when subidividing the example set.
            </summary>
            <param name="sizes">The sizes to segment the data</param>
        </member>
        <member name="M:Neural.ExampleSet.DivideRandom(System.Int32[])">
            <summary>
            Divide the example set into a number of smaller example sets, with 
            the sizes given in an array of values.
            </summary>
            <param name="sizes">The sizes to divide the result sets</param>
            <returns>A shuffled subsets of the original example set</returns>
        </member>
        <member name="M:Neural.ExampleSet.CheckSizeLimits(System.Double[])">
            <summary>
            Checkes the size limits for percentage sizes and throws an exception 
            when necessary.
            </summary>
            <param name="pct">The percentages.</param>
        </member>
        <member name="M:Neural.ExampleSet.DivideRandom(System.Double[])">
            <summary>
            Divide two example sets randomly, using sizes expressed as a percentage of the total size.
            The sum total of all the percentages must be less than or equal to 1.0.
            </summary>
            <param name="pctSizes">An array of percentages</param>
            <returns>The example sets with the percent sizes</returns>
        </member>
        <member name="M:Neural.ExampleSet.DivideLinear(System.Int32[])">
            <summary>
            Divide an example set linearly (by simply iterating), with the given number of elements.
            The total number of elements for each returned sub-example must be less than or
            equal to the number of elements in the original example set.
            </summary>
            <param name="sizes">The sizes of the sub-sets</param>
            <returns>A group of example subsets</returns>
        </member>
        <member name="M:Neural.ExampleSet.DivideLinear(System.Double[])">
            <summary>
            Divide the example sets linearly using the given percentage sizes.  The total sum
            of the percentages must be less than or equal to 1.0.
            </summary>
            <param name="pctSizes">The relative sizes of the subsets</param>
            <returns>The divided subsets</returns>
        </member>
        <member name="M:Neural.ExampleSet.Shuffle">
            <summary>
            Randomize the order of the example set.
            </summary>
            <returns>A randomized copy of this set.</returns>
        </member>
        <member name="M:Neural.ExampleSet.GetEnumerator">
            <summary>
            Return the enumerator to provide foreach support.
            </summary>
            <returns>The examples enumerator</returns>
        </member>
        <member name="P:Neural.ExampleSet.Item(System.Int32)">
            <summary>
            Indexer to provide access to the examples in this set.
            </summary>
        </member>
        <member name="P:Neural.ExampleSet.Count">
            <summary>
            Returns the number of examples in the example set.
            </summary>
        </member>
        <member name="T:Neural.Calculators.ClassificationErrorCalculator">
            <summary>
            Calculates the error given the distance from the actual classification.
            Uses rounding to determine if the values are both in the same class 
            or both not in the same class (1.0 == 1.0) if there are in the same class
            or (0.0 == 0.0) if they are both not in the same class.  
            </summary>
        </member>
        <member name="M:Neural.Calculators.ClassificationErrorCalculator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Calculators.ClassificationErrorCalculator.CalculateError(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Calculate the error based on the correct or incorrect classification.
            </summary>
            <param name="expected">The espected values</param>
            <param name="actual">The actual values</param>
            <returns>The classification error</returns>
        </member>
        <member name="M:Neural.Calculators.ClassificationErrorCalculator.CountClasses(System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Counts the number of indicated classifications in the
            result vector.
            </summary>
            <param name="resultSet">The result set</param>
            <returns>The number of indicated classes</returns>
        </member>
        <member name="M:Neural.Calculators.ClassificationErrorCalculator.GetClass(System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Returns the name of the max value from the error
            set.  If two things are in the same class, their max
            values should be the same.
            </summary>
            <param name="resultSet">The result set</param>
            <returns>The name of the maximum valued attribute</returns>
        </member>
        <member name="P:Neural.Calculators.ClassificationErrorCalculator.DefaultName">
            <summary>
            The default name of this error calculator.
            </summary>
        </member>
        <member name="T:Neural.Listeners.StopOnClassificationListener">
            <summary>
            Stops training when the number of mis-classifications is at or below
            the designated value.
            </summary>
        </member>
        <member name="M:Neural.Listeners.StopOnClassificationListener.#ctor">
            <summary>
            Default constructor
            </summary>
        </member>
        <member name="M:Neural.Listeners.StopOnClassificationListener.#ctor(System.Double)">
            <summary>
            Creates a new listener.
            </summary>
            <param name="stopValue">The value that stops training</param>
        </member>
        <member name="M:Neural.Listeners.StopOnClassificationListener.#ctor(System.Double,System.String)">
            <summary>
            Constructor that takes a stop value and error name.
            </summary>
            <param name="stopValue">The maximum number of allowed mis-classifications</param>
            <param name="errorName">The name of the rror calcualtor</param>
        </member>
        <member name="M:Neural.Listeners.StopOnClassificationListener.EpochEnd(Neural.TrainingEpochEvent)">
            <summary>
            Implements epoch end listeners.
            </summary>
            <param name="evt">The epoch end event</param>
        </member>
        <member name="P:Neural.Listeners.StopOnClassificationListener.ErrorName">
            <summary>
            Sets the error name for the listener.
            </summary>
        </member>
        <member name="P:Neural.Listeners.StopOnClassificationListener.StopValue">
            <summary>
            Returns the number of missclassifications that will stop
            training.
            </summary>
        </member>
        <member name="T:Neural.Listeners.PocketListener">
            <summary>
            A pocket listener saves copies of the network for the
            given error.
            </summary>
        </member>
        <member name="M:Neural.Listeners.PocketListener.#ctor(Neural.Pocket)">
            <summary>
            Creates a new instance of PocketListener
            </summary>
            <param name="pocket">The pocket used by this pocket listners</param>
        </member>
        <member name="M:Neural.Listeners.PocketListener.EpochEnd(Neural.TrainingEpochEvent)">
            <summary>
            Implements the end epoch method.
            </summary>
            <param name="evt">The epoch end event</param>
        </member>
        <member name="T:Neural.Data.FactorScaler">
            <summary>
            Scales a given input value by dividing by a factor.
            </summary>
        </member>
        <member name="M:Neural.Data.FactorScaler.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.FactorScaler.#ctor(System.Double)">
            <summary>
            Constructor that sets the factor to divide by.
            </summary>
            <param name="val">The factor</param>
        </member>
        <member name="M:Neural.Data.FactorScaler.Scale(System.Double)">
            <summary>
            Sclaes the value by the factor.
            </summary>
            <param name="input">The raw input</param>
            <returns>The scaled input</returns>
        </member>
        <member name="P:Neural.Data.FactorScaler.Factor">
            <summary>
            The factor for scaling.
            </summary>
        </member>
        <member name="T:Neural.TrainingException">
            <summary>
            A training exception is thrown during training to stop due to some unrecoverable error.
            </summary>
        </member>
        <member name="M:Neural.TrainingException.#ctor">
            <summary>
            Creates a new training exception
            </summary>
        </member>
        <member name="M:Neural.TrainingException.#ctor(System.String)">
            <summary>
            Creates a new trianing exception.
            </summary>
            <param name="message">The message</param>
        </member>
        <member name="M:Neural.TrainingException.#ctor(System.String,System.Exception)">
            <summary>
            Creates a new training exception.
            </summary>
            <param name="message">The message</param>
            <param name="excp">The parent exception</param>
        </member>
        <member name="T:Neural.TrainingEpochEvent">
            <summary>
            This events is passed to the event listeners so they have access to both
            the trainer and the network, when they are invoked.
            </summary>
        </member>
        <member name="M:Neural.TrainingEpochEvent.#ctor(Neural.Trainer)">
            <summary>
            Creates a new training epoch event.
            </summary>
            <param name="trainer"></param>
        </member>
        <member name="M:Neural.TrainingEpochEvent.#ctor(Neural.Trainer,Neural.Network)">
            <summary>
            Creates a new training epoch event.
            </summary>
            <param name="trainer">The trainer conducting the trainer</param>
            <param name="network">The network being trained</param>
        </member>
        <member name="P:Neural.TrainingEpochEvent.Network">
            <summary>
            The network being trained.  This is sometiems null.
            </summary>
        </member>
        <member name="P:Neural.TrainingEpochEvent.Trainer">
            <summary>
            The network trainer.
            </summary>
        </member>
        <member name="T:Neural.InvalidInputsException">
            <summary>
            This exception is thrown when the inputs to some part of the system do not
            match the expected inputs in the sense of wrong type, wrong number of elements,
            etc..
            </summary>
        </member>
        <member name="M:Neural.InvalidInputsException.#ctor">
            <summary>
            Create a new invalid inputs exception.
            </summary>
        </member>
        <member name="M:Neural.InvalidInputsException.#ctor(System.String)">
            <summary>
            Create a new invalid inputs exception.
            </summary>
            <param name="message">The message</param>
        </member>
        <member name="M:Neural.InvalidInputsException.#ctor(System.String,System.Exception)">
            <summary>
            Creates a new invalid inputs exception.
            </summary>
            <param name="message">The message</param>
            <param name="excp">The parent exception</param>
        </member>
        <member name="T:Neural.Evaluator">
            <summary>
            Summary description for Evaluator.
            </summary>
        </member>
        <member name="F:Neural.Evaluator.errorList">
            The error manager responsible for calculating the error
            values.
        </member>
        <member name="M:Neural.Evaluator.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Evaluator.AddErrorCalculator(Neural.ErrorCalculator)">
            <summary>
            Adds a new error calculator to the evaluator.
            </summary>
            <param name="ec">The error calculator</param> 
        </member>
        <member name="M:Neural.Evaluator.AddErrorCalculator(Neural.ErrorCalculator,System.String)">
            <summary>
            Add an error calculator with the given name, as opposed to the default
            name of the calculator.
            </summary>
            <param name="ec">The error calculator</param>
            <param name="name">The name of the error calculator</param>
        </member>
        <member name="M:Neural.Evaluator.GetError(System.String)">
            <summary>
            Return the error for the given name.
            </summary>
            <param name="name">The name of the error</param>
            <returns>The error</returns>
        </member>
        <member name="M:Neural.Evaluator.Evaluate(Neural.Network,Neural.ExampleSet)">
            <summary>
            Evaluates the network, returning the evaluation of the error.
            </summary>
            <param name="examples">The examples to evaluate</param>
            <param name="network">The network to evalute</param>
            <returns>An error report for the valuation.</returns>
        </member>
        <member name="P:Neural.Evaluator.ErrorNames">
            <summary>
            Returns the error names.
            </summary>
            <value>The error names</value>
        </member>
        <member name="T:Neural.ErrorManager">
            <summary>
            Manages collections of error calculators and their output.  Provides
            summaries of error information known as error reports.
            </summary>
        </member>
        <member name="M:Neural.ErrorManager.#ctor">
            <summary>
            Default constructor
            </summary>
        </member>
        <member name="M:Neural.ErrorManager.AddCalculator(System.String,Neural.ErrorCalculator)">
            <summary>
            Adds a new calculator with the given name to the error
            manager.
            </summary>
            <param name="calculator">The calculator to add</param>
            <param name="name">The name of the calculator</param>
        </member>
        <member name="M:Neural.ErrorManager.AccumulateError(System.Collections.Generic.Dictionary{System.String,System.Double},System.Collections.Generic.Dictionary{System.String,System.Double})">
            <summary>
            Calculates the error using the available error
            calculators.  The results are saved into the error
            report property.
            </summary>
            <param name="actual">The actual network output</param>
            <param name="expected">The expected output</param>
        </member>
        <member name="M:Neural.ErrorManager.GetError(System.String)">
            <summary>
            Returns the error for a given error calculator.  This
            takes the error from the error report.  When training
            is complete this will always report 0, because the
            error report has been moved to the error history.
            </summary>
            <param name="name">The name of the error calculator</param>
            <returns>The error value.</returns>
        </member>
        <member name="M:Neural.ErrorManager.ClearErrors">
            <summary>
            Clears the error report, moving it to the error History.
            </summary>
        </member>
        <member name="M:Neural.ErrorManager.GetHistoricalErrors(System.String)">
            <summary>
            Returns the error history for a given error calculator.
            </summary>
            <param name="name">The name of the error history</param>
            <returns>The history of errors for the given calculator</returns>
        </member>
        <member name="M:Neural.ErrorManager.GetLastError(System.String)">
            <summary>
            Returns the last error value for a given calculator.
            During training the current error value is in the
            error report, and the latest history is in the last
            history record.  After training there is only history.
            </summary>
            <param name="name">The name of the error calculator</param>
            <returns>The error value.</returns>
        </member>
        <member name="M:Neural.ErrorManager.ListCalculators">
            <summary>
            Returns the list of calculator names.  These are all
            the calculators that have been registered with this
            error manager.
            </summary>
            <returns>The list of error calculators</returns>
        </member>
        <member name="M:Neural.ErrorManager.GetCalculator(System.String)">
            <summary>
            Returns an error calculator with a given name.
            </summary>
            <param name="name">The name of the error calculator</param>
            <returns>The error calcualtor</returns>    
        </member>
        <member name="P:Neural.ErrorManager.Count">
            <summary>
            Returns the number of registered calcualtors.
            </summary>  
            <value>The number of error calculators</value>
        </member>
        <member name="P:Neural.ErrorManager.Report">
            <summary>
            Returns the error report, which contains the errors,
            organized by name.  This is moved to the error history
            at the end of each epoch during training.
            </summary>   
        </member>
        <member name="P:Neural.ErrorManager.ErrorHistorySize">
            <summary>
            Returns the size of the error history.  This may be
            a specious function, however, the error history may
            change to a sized window of k error reports, instead
            of all the error reports.
            </summary>
            <value>The sizeof the error history.</value>
        </member>
        <member name="P:Neural.ErrorManager.LastErrors">
            <summary>
            Returns the last error report in history.  During training
            this is different from the current error report.  After
            training there is no current report, just history.
            </summary>
            <value>The last error report in the error history.</value>
        </member>
        <member name="T:Neural.Data.TranslatedField">
            <summary>
            A field that is translated from a set of values.
            </summary>
        </member>
        <member name="M:Neural.Data.TranslatedField.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.Data.TranslatedField.Process(System.String)">
            <summary>
            Processes the input value by returning the translated value.
            </summary>
            <param name="val">Value as a string</param>
            <returns>The values represented by the string</returns>
        </member>
        <member name="P:Neural.Data.TranslatedField.Translator">
            <summary>
            The translator to translate the string.
            </summary>
        </member>
        <member name="T:Neural.Data.OutputField">
            <summary>
            An output field for a reader.
            </summary>
        </member>
        <member name="M:Neural.Data.OutputField.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="P:Neural.Data.OutputField.Value">
            <summary>
            The value of the output field.
            </summary>
        </member>
        <member name="T:Neural.Data.DataReadingException">
            <summary>
            An exception indicating an error during data reading..
            </summary>
        </member>
        <member name="M:Neural.Data.DataReadingException.#ctor(System.String)">
            <summary>
            Create a new data reading exception.
            </summary>
            <param name="message">The message</param>
        </member>
        <member name="M:Neural.Data.DataReadingException.#ctor(System.String,System.Exception)">
            <summary>
            Create a new data reading exception
            </summary>
            <param name="message">The message</param>
            <param name="baseEx">The parent exception</param>
        </member>
        <member name="T:Neural.Data.Column">
            <summary>
            Represents a column of read values.
            </summary>
        </member>
        <member name="M:Neural.Data.Column.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="P:Neural.Data.Column.Name">
            <summary>
            The name of the column.
            </summary>
            <value>The column name</value>
        </member>
        <member name="P:Neural.Data.Column.Scaler">
            <summary>
            The scaler for this colum.
            </summary>
            <value>The column scaler</value>
        </member>
        <member name="P:Neural.Data.Column.IsScaling">
            <summary>
            Identifies the column as performing scaling.
            </summary>
            <value>Inidcates if this column scales values</value>
        </member>
        <member name="T:Neural.Axon">
            <summary>
            The axon that connects two neurons.
            </summary>
            <remarks>
            An axon connects two neurons and propagates the signal to 
            the next layer.  It also transports the feedback information
            during training.
            </remarks>
        </member>
        <member name="M:Neural.Axon.#ctor">
            <summary>
            Default constructor
            </summary>
            <remarks>
            Creates a new Axon with a new id.
            </remarks>
        </member>
        <member name="M:Neural.Axon.#ctor(System.Guid)">
            <summary>
            Constructor with ID
            </summary>
            <remarks>
            The constructor builds a new Axon with the given ID.
            </remarks>
            <param name="id">The axon's id</param>
        </member>
        <member name="M:Neural.Axon.Propagate(System.Double)">
            <summary>
            Propagates the given signal to the dendrite neuron from the base.
            It notifies that dendrite neuron that it has fired, by passing
            itself into the <code>recieveSignal</code> method of the dendrite
            neuron.
            </summary>
            <value>The value to propagate.</value>
        </member>
        <member name="M:Neural.Axon.Feedback(System.Double)">
            <summary>
            When training feedback is fired from the dendrite neuron back to the
            base.  This allows the base neuron to adjust its weight accordingly.
            For middle-tier neurons this consists of the delta * the connecting
            weight.
            </summary>
            <value>The feedback value.</value>
        </member>
        <member name="P:Neural.Axon.Id">
            <summary>
            An axon id.
            </summary>
            <value>The id of this axon</value>
        </member>
        <member name="P:Neural.Axon.Dendrite">
            <summary>
            Axons have a dendrite, to which they pass values when they 
            fire and propagate back feedback to the base neuron.
            </summary>
            <value>The dendrite neuron for this axon.</value>
        </member>
        <member name="P:Neural.Axon.Base">
            <summary>
            Returns the base of the Axon.  The base is the neuron that will fire
            into this axon, sending the signal to the dendrite.  When an
            axon carries a signal, it does so from the base to the dendrite.
            </summary>
            <value>The base of the axon or the source neuron.</value>
        </member>
        <member name="T:Neural.NeuronTraining">
            <summary>
            Neuron training assists the trainer at the level of the individual
            neuron.
            </summary>
        </member>
        <member name="M:Neural.NeuronTraining.SetSize(System.Int32)">
            <summary>
            Sizes the arrays for the weight adjustmets, etc.  Normally,
            this is the size of the inputs to the attached neuron.
            </summary>
            <param name="sz">The size (in inputs) of the neuron to train</param>
        </member>
        <member name="M:Neural.NeuronTraining.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:Neural.NeuronTraining.CalculateDelta(System.Double,System.Double,System.Double)">
            <summary>
            Calculates the delta given the expected values, the actual values
            and the derivative of the activation value.
            </summary>
            <param name="actual">The actual value</param>
            <param name="derivative">The derivative of the activation</param>
            <param name="expected">The expected value</param>
            <returns>The delta value</returns>
        </member>
        <member name="M:Neural.NeuronTraining.CalculateDelta(System.Double[],System.Double)">
            <summary>
            Calcuates the delta given the deltas of the subsequent layers and the
            weight connecting this neuron to the next neuron.  This is used on internal
            or "hidden" neurons to back propagate the error.  Normally this is defined
            as the Sum of the delta[i] * weight[i] for the i-th connection from this
            neuron to the next layer.  We pre-multiply the delta and weight when we
            back-propagate the feedback and therefore we need only sum it.
            </summary>
            <param name="derivative">The derivative of the activation</param>
            <param name="feedback">The feedback (delta and connecting weight)</param>
            <returns>The delta value</returns>
        </member>
        <member name="M:Neural.NeuronTraining.ClearPartials">
            <summary>
            Clears the partial derivatives, called after weights are adjusted.
            </summary>
        </member>
        <member name="M:Neural.NeuronTraining.CalculatePartials(System.Double[],System.Double)">
            <summary>
            Initializes the partials, if necessary.  Calculates the partials
            by summing them.  During an epoch the partial derivatives are cumulative.
            For on-line training, they are recalculated with ever input.
            </summary>
            <param name="delta">The calculated delta for this neuron</param>
            <param name="inputs">The inputs for this training example</param>
        </member>
        <member name="M:Neural.NeuronTraining.UpdateWeightAdjustments">
            <summary>
            Updates the weight adjustments.  The weights are passed in only for
            the size.  Could be changed to an int parameter.  Could also pre-
            size the weight adjustment array size earlier.  Also stores off
            the prior weight adjustents
            </summary>
        </member>
        <member name="M:Neural.NeuronTraining.EndEpoch">
            <summary>
            Called to end the epoch.  Clears the partial derivatives.
            </summary>
        </member>
        <member name="M:Neural.NeuronTraining.AdjustWeights(System.Double[])">
            <summary>
            Called to adjust the weights.  Used the weight adjustments to update
            the weights passed in by reference.
            </summary>
            <param name="weights">The weights to adjust</param>
        </member>
        <member name="P:Neural.NeuronTraining.Trainer">
            <summary>
            The trainer that owns this neuron trainer.  Neuron trainers are children
            of trainers, much the same way that neurons are the children of networks.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.WeightAdjustments">
            <summary>
            Return the weight adjustments.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.PriorWeightAdjustments">
            <summary>
            Read only copy of the prior weight adustments.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.PartialDerivatives">
            <summary>
            Returns the partial derivatives.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.LearningRate">
            <summary>
            The learning rate used by this Neuron trainer.  Currently
            all neuron trainers attached to a given trainer use the same
            rate, but that may change in the future.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.Momentum">
            <summary>
            The momentum coefficient used to calculate the weight change.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.Partials">
            <summary>
            Return the partial derivatives.  This function seems pretty
            useless.
            </summary>
        </member>
        <member name="P:Neural.NeuronTraining.WeightUpdates">
            <summary>
            Returns the weight updates - anther specious function.
            </summary>
        </member>
        <member name="T:Neural.Managers.GenericManager">
            <summary>
            The generic manager is a delegate for the particular managers.
            </summary>
        </member>
        <member name="M:Neural.Managers.GenericManager.#ctor">
            <summary>
            Default Constructor.
            </summary>
        </member>
        <member name="M:Neural.Managers.GenericManager.AddObject(System.Object,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Add a managed object, passing in the object, the storage tag and the object
            name as a string.
            </summary>
            <param name="name">The name used to identify the object</param>
            <param name="obj">The object</param>
            <param name="tag">The data storage tab</param>
        </member>
        <member name="M:Neural.Managers.GenericManager.AddObject(System.Object,System.String)">
            <summary>
            Add an object to be managed by the manager, passing in the object and the
            name.  The data storage tag is the default data storage tag.
            </summary>
            <param name="name">The name of the object</param>
            <param name="obj">The object to maange</param>
        </member>
        <member name="M:Neural.Managers.GenericManager.CleanVault(System.String)">
            <summary>
            Cleans out the vault for the given file extension.
            </summary>
            <param name="extension">The file extension to remove</param>
        </member>
        <member name="M:Neural.Managers.GenericManager.GetObject(System.String)">
            <summary>
            Return an object from the vault.
            </summary>
            <param name="name">The name of the object</param>
            <returns>The object</returns>
        </member>
        <member name="M:Neural.Managers.GenericManager.GetStorageTag(System.String)">
            <summary>
            Returns the data storage tag for this object.
            </summary>
            <param name="name">The name of the object</param>
            <returns>The data storage tag</returns>
        </member>
        <member name="M:Neural.Managers.GenericManager.ListFilenames">
            <summary>
            Lists the names of the stored objects.
            </summary>
            <returns>The list of stored objects</returns>
        </member>
        <member name="M:Neural.Managers.GenericManager.RemoveFile(System.String)">
            <summary>
            Removes the object from the vault.
            </summary>
            <param name="name">The name of the object</param>
        </member>
        <member name="M:Neural.Managers.GenericManager.UpdateFile(System.String,System.Object)">
            <summary>
            Update the object with the given name.
            </summary>
            <param name="data">The object data</param>
            <param name="name">The name of the object to update</param>
        </member>
        <member name="M:Neural.Managers.GenericManager.UpdateFile(System.Object,Neural.Managers.DataStorageTag,System.String)">
            <summary>
            Update the file with the given name, with new data storage tag and new data.
            </summary>
            <param name="data">The object data</param>
            <param name="name">The name of the data object</param>
            <param name="tag">The storage </param>
        </member>
        <member name="P:Neural.Managers.GenericManager.Extension">
            <summary>
            Sets the file extension of the type of data to be stored.  Multiple types of
            files can share the same vault - but must have different extensions.
            </summary>
        </member>
        <member name="P:Neural.Managers.GenericManager.VaultDirectory">
            <summary>
            Returns the vault directory.
            </summary>
        </member>
        <member name="T:Neural.Data.ClassificationScaler">
            <summary>
            Scales classification values to a given interval.
            </summary>
        </member>
        <member name="M:Neural.Data.ClassificationScaler.#ctor">
            <summary>
            Default contructor.
            </summary>
        </member>
        <member name="M:Neural.Data.ClassificationScaler.#ctor(System.Double,System.Double)">
            <summary>
            Constructor that specifies a range for the scaling.
            </summary>
            <param name="min">The minimum value for the scale</param>
            <param name="max">The maximum value for the scale</param>
        </member>
        <member name="M:Neural.Data.ClassificationScaler.Scale(System.Double)">
            <summary>
            Scales the input value.  Given an input value scales it to the appropriate
            response.  Overrides the abstract method on Scaler.
            </summary>
            <param name="input">The unscaled input value</param>
            <returns>The scaled value</returns>
        </member>
        <member name="P:Neural.Data.ClassificationScaler.MaxScale">
            <summary>
            The maximum value for the scale.
            </summary>
        </member>
        <member name="P:Neural.Data.ClassificationScaler.MinScale">
            <summary>
            The minimum value for the scale.
            </summary>
        </member>
    </members>
</doc>
